Threshold segmentation of PCB defect image grid based on finite difference dispersion for providing accuracy in the IoT based data of smart cities

Ke Fang ()
Additional contact information
Ke Fang: Henan Logistics Vocational College

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 13, 131 pages

Abstract: Abstract The image segmentation is a process of segregating image into manageable partitions or segments. Image segmentation involves segmentation of foreground from background, based on similarities in features, color or shape. Segmentation is the foremost step in the scrutiny of the printed circuit boards. The segmentation technique basically partitions the PCB into small chunks containing primeval PCB sub-patterns. The conventional threshold segmentation technique considers the effect of image noise and grayscale in the threshold segmentation operation of the PCB (printed circuit board). In this paper, we have tried to overcome the problem of conventional segmentation for PCB such as grayscale overlapping and noise related issues in the segmented images. The basic formula of the optimal threshold operation is given to the threshold segmentation algorithm, the triangular image grid model is designed, the finite difference discrete method is exploited, the optimal threshold calculation formula is devised, and an attempt is made to get the best threshold for segmentation of PCB. Through experimental analysis for the PCB circuit image, two-dimensional OSTU threshold segmentation method, two-dimensional Fisher threshold segmentation method and the proposed research method are used to perform image grid threshold segmentation and to evaluate the performance of the proposed and the existing methods. The experimental results show that the threshold segmentation values obtained by the finite difference based threshold method has low image-noise effect, the gray scale overlap is also minimized, and segmentation accuracy is more as compare to other benchmarked threshold methods.

Keywords: Threshold segmentation; Image noise; Image grayscale; Optimal threshold; Grayscale difference (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01296-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01296-4

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01296-4

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().